Cosmetic male genital surgery: a narrative review
Bibliographic record
Abstract
Background and Objective: Through the centuries the appearance of the male genitalia has always been an important concern for men, symbolizing virility, potency and sexual contentment. Correction of perceived deficiencies and deformities of the male genitalia can be addressed by aesthetic surgery as well as the enhancement its external aspect. If the social acceptance of cosmetic surgery, particularly of women's breasts, dates from the early 1950s, male intimate cosmetic surgery emerged from the shadows about 10 years ago with a medical community still very suspicious and reproachful. The present paper aims to describe and discuss the current state of the art regarding male intimate cosmetic surgery. Methods: A narrative review of the literature was performed using publications from January 2000 to September 2022. The publications were retrieved from the PubMed database using Medical Subject Headings (MeSH) terms and keywords. The authors' goal is to narrate the aesthetic non-surgical and surgical enhancement procedures of the male apparatus. Key Content and Findings: This narrative review examines the diverse procedures associated with male genitalia aesthetics. Conclusions: Aesthetics of the male genitalia is now an unavoidable and important part of aesthetic surgery worldwide with an increasing demand. Nonsurgical and surgical techniques described in the literature should be reviewed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".